In this section, we visualize the feature distributions in latent space aiming to confirm the effects of our method. To this end, we first select two similar images (\(x_S\)  and \(x_T\) ) from source and target domain respectively and then map their high-dimensional latent features (\(z_S\)  and \(z_T\) ) to a 2-D space with t-SNE [1]} shown in Fig. REF . In the first row, we label the t-SNE maps by different domains in order to evaluate the marginal distribution alignment (global alignment) of the features between domains. While in the second row, we label the t-SNE maps by different semantic classes in order to evaluate the semantic consistency (local alignment) of the features between domains.
